where \({{F}_{\mathcal {C}}}\)  is a segmentation function of clean model \(\mathcal {C}\)  that projects the image to the prediction. Following many noisy label learning approaches which consider samples with smaller loss as clean ones [1]} [2]} [3]}, we set a threshold \(\theta \)  to distinguish the labels with small loss as the clean labels and then use these clean labels as the supervisory information for training GAT.
